These kinds of parses are quite different in terms of both the head-finding rules and the dependency relations.

we set the parameters involving dependency path modeling as task-specific, i.e., BiLSTM gen , BiLSTM rel and W sp (Figure 2).


Derivation is a very productive process in many languages, notably Slavic languages.

natural language processing (NLP) for these languages can profit from knowledge about derivational relationships (Green et al., 2004;Szpektor and Dagan, 2008;.


This study had its own limitations, though, since it considered only 6 derivation patterns, all within-POS.

it remains unclear to what extent the differences between the two studies are due to (a) the language difference, (b) prefiltering word pairs, or (c) the choice of derivation patterns under consideration.


The relative importances of these effects is however only weakly indicated by the sizes of the coefficients.

the column LMG provides normalized Lindeman-Merenda-Gold (LMG) scores (Lindeman et al., 1980), a measure of effect size (Grömping, 2012), applied, e.g., by Marelli et al.


This model simply learns word embeddings which are then summed into a single vector and projected to the target image vector.

this model does not have access to word sequence information, and is a distributed analog of a bag-of-words model.


Such cases are the most frequently occurring LVCs in Hindi, but do not represent all LVCs.

the application of diagnostic tests for LVCs for a large corpus can be challenging.


(2011) describe a classifier for Hindi LVCs but do not mention the distribution of LVCs in the data.

it is difficult to know whether the results are applicable to all LVCs or just the light verb 'kar'.


These alternations show that a collocational measure that only looks at the bigram occurrences may not be able to capture a noun-light verb alternation that is relatively infrequent.

linguistic information would be required to identify a predicating nominal that appears in a number of contexts.


This is probably because of the lack of reliable linguistic diagnostics mentioned earlier in section 2.

we can think of the Hindi Treebank annotation of LVCs as reflecting a fairly generous conceptualization of LVCs.


is one token, but three in the Dundee Treebank.

eye movement measures are only available for the entire visual unit.


To deal with variable feature size, we perform a max-pooling operation over c o by selecting the maximum value c o = max c o .

after these two operations, each filter generates only one feature.


The local-3 function worked better than the max function in term of the MAP, and also got a comparable MRR.

we use the local-3 function in the following experiments.


After that, adding more complex filters decreased the performance.

the trigram is the best granularity for our model.


After adding some word overlap features between the two sentences, the performance was improved significantly (the third row of Table 2).

the lower-level granularity is an indispensable factor for a good performance.


The set of user generated categories for each e 2 E ⇤ is denoted as Cat(e).

we need to design a learning algorithm F based on R ⇤ to predict whether there is an is-a relation between e and c where e 2 E ⇤ and c 2 Cat(e).


In each cluster, we assume is-a relations share the same projection matrix and vector offset.

we aim to learn K projection matrices and offset vectors as representations of is-a relations.


Based on the proof in (Khuller et al., 1999), the objective function is monotone and submodular.

we design a greedy relation selection algorithm to solve this problem with the accuracy of 1 1 e , shown in Algorithm 1.


The π t are set to some shared set of sense probabilities.

initially the word distributions for each sense k are almost identical, and the sense distributions are the same at all times, so far from the neologism situation.


The Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) or the more recently introduced gated recurrent unit (GRU) (Chung et al., 2014) use gates to control the information flow from previous words.

lSTM and GRU are better at capturing longterm dependencies than simple RNNs, and are often chosen in practice.


It is also worth mentioning that the proposed LMs are trained from scratch on ID training data, but adapting knowledge from the GD word vector model.

the hidden state in Equation 6is not strictly the "GD hidden state"; it uses the word embeddings from the GD pre-trained word vector model, but the inputs are still ID data.


Weighted Context Vector Concatenation (WCVC): we can extend the CVC approach by applying a concatenation weight on h * t in Equation (6).

the network can have simple control over the amount of the information flowing from GD, as in Equation 10: 3.


Weighted Context Vector Sum (WCVS): another approach is to apply a weight vector on the GD context vector.

the information from GD can be controlled before compacting, as in Equation 12: Figure 4: RNN LM with gated adaptation, where indicates the gates mechanisms described in Section 3.3.


Ideally, we want to have an adaptation mechanism that can sequentially adapt the information from GD for the sequence inputs.

we propose various gated domain adaptation mechanisms.


Adding up the context representations in CVS is also more useful than concatenating the context representations in CVC.

we can draw the conclusion that information from GD should be compressed (summed) into ID rather than using scattered (concatenated) representations.


We think this is because the adapted representation is a newly computed vector after the sum operation, where the weight vectors are hidden behind the sum operation.


Furthermore, the computation of the current adapted representation also involves h t−1 and h * t 1 .

the adaptation histories are managed by the model and sequentially traverse along the input string.


At each step during sequence prediction, a translation system outputs a full probability distribution over the target vocabulary.

the task of NMT system combination can be cast as an ensemble prediction task and a variety of existing general prediction combination methods can be applied.


The general idea behind ensembles is that different predictors are likely to produce slightly different errors for different input instances, and if their predictions are combined, the overall error is reduced.

it is essential to introduce a minimum of diversity into an ensemble.


The training data for learning the ensemble combination function and the test set should necessarily be fully parallel (tri-parallel).

we extracted our test set from the available trilingual data and did not use the test sets provided by (Cettolo et al., 2012) since they are not parallel across all three languages.


y E = comb(y 1 , ..., y m ) We would like the method to be applicable to a situation where a trilingual parallel corpus, i.e., the corpus needed to train a multi-source combination function, is a scarce resource, which is a realistic assumption.

we are interested in combination functions with a small number of trainable parameters.


Compared with previous phrase-based decoding methods, our method is based on a simple left-to-right dynamic programming in which no decoding-time reordering is performed.

its runtime is very fast and implementing the algorithm becomes easy.


It is guaranteed that this preordering lattice is uniquely determined given a set of preordering candidates A, and the final graph structure does not depend on the integrating order.

we can merge new lattice paths by enumerating preordering candidates one-by-one.


This tendency is expected, because ignoring confidence scores of preordering candidates implies treating all preordering candidates with the same importance, and the decoder have finally chosen the hypothesis with accidentally high scores by other features.

introducing preordering confidence into decoding features is effective to prevent these kind of errors and guarantee translation accuracies.


In this case, the language model of "image information" is stronger than that of "information and images" and this difference of scores exceeds the preordering confidence.

the decoder fails to choose correct preordering.


Such forums are usually open, allowing any user to respond to a given question.

for a question posed by the user, the quality of response often varies a lot -ranging from highly precise and detailed answers given by authentic users to highly imprecise or non-comprehensible one-word or single line answers posted by spammy and other nonserious users.


They observed that event facet phrases and event expressions tend to co-occur in event introductory sentences.

the bootstrapping system first learns event expressions from the sentences that contain both types of event facets and then learns more event facet phrases from the sentences that contain an event expression and a different type of event facet, in an iterative manner.


Note that our method has already outperformed H&R's best result at the third iteration (73.3% in recall and 79.6% in F1-score) while H&R's method achieved this performance after the 4th iteration.

with bilingual structure projection, the number of iterations of the original bootstrapping learning process can be decreased.


For example, a summarization or information extraction system on terrorism attacks may only concern with a few event types (e.g., Attack, Die, and Injure).

annotating an event-driven fully-annotated temporal relation corpus becomes a crucial issue to the success of real-life applications.


Specifically, only a few studies apply global inference models to exploit temporal relations in discourse level.

how to acquire discourse-level temporal information from those long-distance event mention pairs in nonadjacent sentences becomes another crucial issue to temporal relation extraction, especially for Chinese, as a discourse-driven language with a broad range of ellipsis and flexible sentence structures.


For the first problem, we relax the expressedat-least-once assumption, and instead assume that " a relation holding between two entities can be either expressed explicitly or inferred implicitly from all sentences that mention these two entities" (see Figure  1 for a simple illustration).

after automatically extracting features within each sentence using a convolutional architecture, we employ cross-sentence max-pooling to select features across different sentences, and then aggregate the most significant features into a vector representation for each entity pair.


The filtered version prunes the original NYT10 data slightly by removing (1) duplicated sentences for each entity pair, (2) sentences which have more than 40 tokens between a pair of entities, and (3) sentences with entity names that are substrings of other entity names in Freebase.

some relations with low frequency are removed.


We assume that a good multi-document summary is supposed to reconstruct the main content of the document set.

we focus on two issues: (1) how to represent the main content of the document set, and (2) how to use the summary set to reconstruct the main content.


But this yields large and complex planning encodings, and it is not clear how to exploit those effectively.

we take a different approach here, pre-compiling all combined (nonatomic) categories that will be considered by the planning process.


Alternative Approaches Having trained humans, e.g., professional abstractors, write multidocument summaries is expensive, and for laymen, the task is highly demanding and time-consuming.

a recent project on the construction of a MDS corpus for European Portuguese took a semi-automatic approach where the documents within a topic were first filtered for relevance using a summarization system and then processed by human annotators who only had to remove sentences until the summary length was reached (Almeida et al., 2014).


In combination, we see that there are a lot of sentences in hMDS with both very low precision and very low recall.

we can conclude that we indeed constructed a corpus containing sentences which do not contribute much to a good summary (see section 3.1, presence of unimportant information).


We identify each aspect, its relevant sentiment and the target location entity jointly by introducing a new polarity class called "None".

"None" indicated that a sentence does not contain an opinion for the aspect a of location l. the overall task can be defined as a three-class classification task for each (l, a) pair with labels "Positive", "Negative", "None".


When an example is well developed, the nodes corresponding to this development will form a well connected sub-graph.

there will be a shift in the distribution of PageRank values to reflect the fact that the random surfer is likely to visit these nodes with comparable probabilities as the nodes of the main theme or stance.


Indeed, it is impossible to develop an idea or character without using additional words.

longer essays may receive higher scores.


Significant improvements are witnessed when n-gram and weighting techniques are introduced into neural bag-of-words models.

new strong baselines are achieved on 5 document-level datasets and 4 sentence-level datasets.


However, traditional sparse BOW representations take each word or n-gram as a unit and ignore the internal semantics of them.

they tend to generalize poorly compared with the newly proposed distributed representations.


To introduce weighting techniques into distributed case, weighted objective function is optimized to train text embeddings: where Weight(•) denotes the weight of n-gram •.

text embeddings are trained to predict those important n-grams in larger probabilities rather than those words that have little discriminative information for classification.


Features extracted by CNN can therefore be used instead of hand-crafted features, as they carry more useful information.

the idea behind convolution is to take the dot product of a vector of k weights w k also known as kernel vector with each k-gram in the sentence s(t) to obtain another sequence of features a max pooling operation is applied over the feature map and the maximum valueĉ(t) = max{c(t)} is taken as the feature corresponding to this particular kernel vector.


We concatenate the feature vector of each personality dimension in order to create the final feature vector.

the personality model ultimately extracts a 750-dimensional feature vector (150-dimensional feature vector for each of the five personality traits).


This not only prevents our model to overfit the data but also speeds up the computation.

we obtain an improvement in the overall performance with automatic feature extraction using a relatively lower dimensional feature space.


The creation of these resources requires time, effort and a considerable monetary investment in order to ensure the quality and subsequent usefulness.

finding a way to perform sentiment analysis for under-resourced languages without having to repeat these efforts is an interesting endeavor.


However, this does not capture the true granularity of opinionated text.

in this paper we focus on CLSA at aspect-level 1 Document-and sentence-level CLSA assume that an entire section of text expresses one sentiment towards one entity.


Aspect-based CLSA In under-resourced languages, we lack resources and NLP tools which would allow us to create stateof-the-art systems similar to those mentioned.

the ability to leverage resources that already exist in English to perform sentiment analysis in other languages would be a great advantage.


In our approach, however, all of the weight of correctly classifying a phrase fell on the accuracy of the mapping scheme.

it seems that any error in the mapping resulted in the propagation of error during classification.


As for comments, we view them as short documents with authors only but without likers nor their own comments 3 .

we apply document composition on comments although here users are commenters (users who comment).


Instead of requiring that the comment stance agree with the post, UTCNN simply extracts the most important features of the comment contents; they could be helpful, whether they show obvious agreement or disagreement.

when combining comment information here, the maximum pooling layer is more appropriate than other pooling or merging layers.


This is supported by its outperforming other models though no oversampling technique is applied to the UTCNN related experiments as shown in this paper.

we can conclude that the user information provides strong clues and it is still rich even in the minority class.


This state provides the best scenario for further enlarging their skills.

the state of flow is in in continuous motion, as skills evolve over time.


Communication success progressively improves as population succeeds in aligning their construction inventory and membership prototypes.

of this alignment, alignment success also increases and reaches the same value as communicative success.


Although the empirical results (section 4) we obtained were rather promising, there remains considerable room for improvement.

the paper concludes with possible future directions (section 6) after a brief review of some related research (section 5).


As displayed in the table, Ma's original dataset contains a relatively small number of "no-evocation" instances that causes the problem of skewed distribution.

as a remedy, we added the synset pairs of which the evocation strength was rated as zero in the PWN dataset to this category, finally giving us a data set of 428,790 synset pairs.


The whole combination of the proposed features yielded the best results of r = 0.4391; ρ = 0.4000 with NN, which significantly outperformed the results with RF, r = 0.3695; ρ = 0.3291.

in the following, we only discuss the results achieved by NN.


The combination of all the proposed features yielded 0.8703 in total accuracy with RF, which is considerably more accurate than 0.7642 with NN.

in the following, we only discuss the RF results 11 .


(2) For successful pattern recognition, we need to mine argumentative structure effectively.

we omit potentially helpful structure such as attack relations, as all available data seems insufficient for reliably training respective approaches.


This might explain why the morph-trans approach works quite well and competitive in morphological analysis tasks, as re-confirmed in our experiments.

long-range dependencies might actually be a more crucial aspect for the performance of the model presented by Faruqui et al.


But, these resources are either scarce in some languages or specific to a few number of domains.

recently query-dependent collections have been shown to be more effective and are available to many languages Ganguly et al., 2012).


Since {monde, coupe, 2018} are conceptually better translations, we expect them to appear in F .

the presence/absence of the translations in F can be indicators of their relevance to the query.


On the other hand, appearing [cerveau] as a relevant observation in D, lead L2R to distribute translation probability more justly between [tete] and [cerveau].

we believe that L2R defines a reliable hyperplane discriminating between the context words and the noisy ones more effectively.


Convolution-maxpooling slides a window over the input and identifies the best matching subsequence for a task, using a number of filters that support flexible matching.

convolution-maxpooling is an appropriate method for finding the pattern subsequence that best matches the predicate description.


Even in this restricted domain there are a large number of paraphrases of the same question.

the problem of mapping from a question to a particular predicate and entity in Freebase is hard.


12 can be written as: Since p is a value between zero and one, sum components that are far away in the past are multiplied by a very low value and are effectively removed from the summation.

even though the network is able to learn long-term dependencies, it is not capable of exploiting them during test phase.


A strong binding signifies high probability for the terms to have been generated by the same topic.

as we show, the conditional independence of topics given the per-document topic distributions does not hold.


, F p are all continuous, then C is unique.

any multivariate distribution F can be decomposed into its marginals F i , i ∈ {1, .


The Cloze-style reading comprehension problem (Taylor, 1953) aims to comprehend the given context or document, and then answer the questions based on the nature of the document, while the answer is a single word in the document.

the Cloze-style reading comprehension can be described as a triple: where D is the document, Q is the query and A is the answer to the query.


The purpose of automatic aggregation models such as those decribed in Section 2 is to arrive at the same judgment with a few annotations that a simpler scheme like majority vote would have arrived at given many annotations.

a crowd-constructed gold standard is appropriate for evaluation of such models.


In order to help MT systems collaborate with human translators more effectively, researchers carried out the study on computer-assisted translation (CAT), in which the main goal of MT is supporting professional translators in improving their productivity.

the ability to interact with human becomes an important research topic in CAT.


In this way, the system can be more error-tolerant.

we built the model in the latter way and adopted the phrase-based model (PBM) as in (Barrachina et al., 2009).


Because there are re-orderings in the translation, the positions of these words are perhaps not continuous.

the translation hypotheses should meet the following requirements: a) the positions of the source words covered by p 2 are fully contained in the span of the subtree; or b) the positions of the source words covered by p 2 are continuous and are located at the tail of the source words covered by the subtree, and the source words covered by p 1 fully cover the remained positions of the subtree.


However, there exist many mistakes in the parsing results.

strictly following the subtree constraints according to the parsing results will lead to the loss of some good hypotheses.


In NMT, words are represented as vector-space representations.

it is very natural to use them together.


h j is defined in Equation 3: where c is the source context vector computed by the encoder, t j−1 is the word vector representation of word j − 1, h j−1 is the hidden state for time j − 1 and g is a non-linear activation function.

we use the source input sentence and previous translated words to predict the next word.


Since the machine translation system is not perfect, the output of the system may not be correct translation containing errors possibly.

we will call the output language of the PBMT system e .


In both cases, we can no longer train the NMT system on the source language and target language data, but on the output of the PBMT system and the target language data.

we need to generate translations of the whole parallel training data using the PBMT system.


Indeed, all the methods evaluated in Section 3.2 consider that all the occurrences of a word (or lemma) should result in one thesaurus entry, that is, no disambiguation is performed.

one could suspect that the list of distributional neighbors of a polysemic word would be impacted.


An interesting property of the representations learned by SG, CBOW and GloVe is that they capture similarity as well as analogies and related linear relationships.

both word similarity and word analogy tasks are now commonly used to evaluate the quality of word embeddings.


As already mentioned in the introduction, while our model also represents words as densities, our densities model the uncertainty about the true location of a word vector, rather than modelling the diversity of the underlying concept.

for instance, Kullback-Leibler divergence is meaningful for modelling word similarity in the approach from (Vilnis and McCallum, 2015), but would not be appropriate in our model.


On the other hand, evaluating (1) for every pair (i, j) is not feasible as it would make the complexity of the model quadratic.

we let J i contain all indices j for which x ij > 0, as well as a random sample of indices for which x ij = 0.


For instance, the word "yellow" is a very strong associate of "banana", but the two words co-occur relatively infrequently since most bananas are yellow.

modifying the word banana with yellow is uninformative, so most people leave it out when talking.


We tuned other hyperparameters, e.g., λ in our model and k in k-means, with the validation data.

of several preliminary experiments, we decided other hyperparameters as well.


Note that we repeat the linear transformation of the state cell of mention m i (i.e., W m i h m i ) t times.

each column in M is the attention representation for each preceding mention m j (i − t ≤ j < i) by combining the output vector h m i of mention m i and the output vector of mention m j (j's column vector in H).


For each mention, we set the maximum number of its context mentions as 50 5 .

we unfold the network 51 times and apply masking for the instances which have less than 50 context mentions.


(2013) observe that bridging anaphors are rarely marked by surface features.

they carefully design discourse structure, lexico-semantic and genericity detection features to capture the phenomenon.


In addition, only a few of them have antecedents occurring in the preceding ten mentions.

the model seems mainly uses the last output vector (h m i in Equation 11) for prediction.


Firstly, there are more discourse connectives in Chinese than in English and their parts of speech have more varieties (Huang et al., 2014).

it is likely to encounter words that have the same surface forms as real connectives but do not function as discourse connectives.


Directly matching with raw text would yield 12,498 candidate components 2 because many characters used for connectives appear in other unrelated words.

stanford Chinese segmenter (Chang et al., 2008) is employed to segment paragraphs into tokens.


Although correct EDU segmentation is unavailable to our system because we attempt to extract arguments from raw texts, the EDU boundaries in CDTB only occur with certain punctuation symbols that separate phrases and sentences.

we segment a paragraph by these symbols, and solve the sequence labelling problem on these segments instead of EDUs.


When testing, CRFs are used to label the segments for each connective we extracted.

the same segments for a paragraph are labelled independently for each explicit relation inside the paragraph.


It involves several complex and interacting factors, touching upon all layers of linguistic analysis, from syntax, semantics up to pragmatics.

also annotation is complex and time consuming, and hence available annotated corpora are sparse and limited in size.


Note, however, that in a sequence prediction model we have no guarantee that our output structures form well-formed discourse trees.

we use the following heuristics to guarantee well-formed output structures: Heuristics The first three heuristics are enough to guarantee well-formed trees in practice: 1) If the first predicted label only contains closing parenthesis, we replace them by opening ones.


Enhanced Word Embedding For obtaining word representations, we enhance ordinary word vectors with the character-based vectors obtained from the character-level module by concatenating all the representations on words.

a sequence of enhanced word embeddings could be obtained, which could cover contextual information from character-level to word-level, and the character-based embedding could alleviate rare word problems in some way.


In order to be in consistence with previous setting, we also remove 5 Types which are too few in the corpus: CONDITION, PRAGMATIC CONCESSION, PRAGMATIC CONCESSION, PRAGMATIC CONTRAST and EXCEPTION.

we use the remaining 11 Level 2 Types in our experiments.


Somewhat surprisingly, the model of BiLSTMs seems not good for this sentence-pair modeling task, the reason might be that using the last states of LSTMs ignores too much information of the  previous words of the sequence.

for the rest models, CNN will be selected to compute sentence vectors.


However, the character forming of a word could be also helpful, especially when we are dealing with rare words.

in the fourth group, we will explore the combination of character-level and word-level representations.


Assigning types requires annotators to perform abstract reasoning often with incomplete information; systems must do the same.

the choice of representation for temporal relation types is critical.


It is important to get specificity right in a given relation set, and to select carefully the groupings made when creating coarser relation sets.

being aware of the expressiveness of different representations is critical to choosing how to represent temporal relations effectively for a given scenario.


Note that at test time, the CDEC system is not aware of the layout of the dataset so that it does not exploit the partitioning of documents into topics and sub-topics when making coreference decisions.

a system can (incorrectly) link two event mentions across topics (or sub-topics), for which it should be appropriately penalized in evaluation.


Similar to prose text, lyrics are a discourse: i.e., they are, typically, a sequence of related lines, rather than an unconnected stream of lines of arbitrary order.

like texts, lyrics also have a discourse structure consisting discourse segments.


In case of the Hindi songs, it is difficult to annotate mood of 30 second song clips since the annotators get confused in between adjacent mood classes while annotating short duration audio files.

we sliced each of the audio files into 60 second clips.


Subsequently we removed the duplicate lines as it was observed that the starting stanza is usually repeated in the song at least a few times.

we removed these duplicate sentences to remove the biasness in the lyric.


Feature level correlation (Hall, 1999) was used to identify the most important features as well as to reduce the feature dimension in (Patra et al., 2015a).

we used the correlation based supervised feature selection technique implemented in Weka toolkit 7 to find out the important contributory feature set for audio and lyrics.


However, there were less number of instances present in the Western music as compared to the Hindi music.

we developed another pair of mood classification systems using the same number of instances for both the Hindi and Western music.


Instead of answering how to incorporate context information, we try to understand when we need the information.

our effort is complementary to the existing work on response generation.


We considered the following methods as baselines: Length: intuitively, short messages tend to be context dependent (e.g., "why" in Table 2).

we employed length of a message as a baseline.


The representation is easily to be biased by some specific words like "Saturday night" in the example.

both SVM (classification) and SVM (regression) failed on this case.


However, as is the case in general with pipeline based architectures, such a system is bound to have a fair amount of error propagation, where errors in labeling the acts or subacts affect the performance of mode labeling.

we propose a novel joint inference method for this task where we label modes jointly with dialogue acts and subacts, thereby taking advantage of the inter-dependencies between them.


Clearly, this is a formula which would not hold true for all possible instantiations.

we specify a soft formula of the form, An important aspect to note about the above soft formula is the "+" sign for variables in the formula.


The task of DA classification (sometimes called DA identification) is to attribute one member of a predefined DA to each given utterance.

dA classification is sometimes treated as short-text classification.


As can be seen from Table 2, the positive trend of increasing accuracy fails after setting λ = 0.8.


• We propose an attention mechanism that attends the key information in question conditioned on the current decoding state of the decoder.

the question embedding is dynamically changed over the decoding states.


However, due to the limits of the model and the lack of semantically-annotated corpora, the statistical approach cannot ensure the adequacy and readability of the generated language.

the statistical approach has not yet been employed widely compared to the rule-based or template-based methods.


In the reranking phase, the candidate set is reranked by the sentence score.

the top-n responses of the candidate set are chosen as the output responses.


The key idea is to remove the need for distinguishing domains by explicitly predicting topics/schema of queries.

we obviate the need and directly predict the schema from queries by multi-label classification (either with an RNN or with binary logistic regressions).


In given a query, "order small pepperoni from domino", the word "small", "pepperoni" and "domino" is tagged as size, product and place name, respectively.

lSTM multi-label classification model should predict a set of slots a query would be tagged with.


Further, it is widely known that in English, roughly half of all tokens in a given corpus occur only once.

it is essential to handle rare words properly to obtain good performance.


(2014) considers the counts of the top K characters in each string.

if the "root morphemes" are long enough to create nontrivial count statistics, this metric may, too, favor a more linguistic similarity, but as before, shorter strings could have unwanted effects.


(2002) create automatically-generated features based on sequences of characters within the strings to be compared.

those sequences that do not cross morpheme boundaries could be especially helpful for estimating morphological similarity.


(2015) show that deep convolutional neural networks (CNN) are more powerful than SVMs and standard feedforward neural networks.


In this paper we explore the idea that permutations factorizing into (on average) shorter primal permutations should represent simpler ordering as well.

we contribute Permutation Complexity, a class of metrics over PETs and their extension to forests, and define tight metrics, a sub-class of metrics implementing this idea.


From our Permutation Complexity viewpoint we see PET factorization as compression using a code book of primal permutations.

we introduce tight metrics, a sub-class that assigns a smaller complexity to a PET than to any less factorized structure of the same permutation, with the intuition that more factorization should reveal simpler building blocks.


Our work is built upon the attention-based NMT (Bahdanau et al., 2015), which takes a sequence of vector representations of the source sentence generated by a RNN or bi-directional RNN as input, and then jointly learns to align and translate by reading the vector representations during translation with a RNN decoder.

we take an overview of the attention-based NMT in this section before detail the NMT IA in next section.


We take the annotations of source sentence as a memory instead of using an external memory, and we design a mechanism to directly read from and write to it during translation.

the original source annotations are not accessible in later steps.


The literature result for CREE (Meurers et al., 2011a) is not completely comparable, as it was computed on the held-out test set that does not satisfy the unseen question task.

it is not surprising that our model does somewhat worse on a purely unseen question evaluation.


The two examples show that the summarized opinion representations are not able to differentiate the opinion influences of different expressions on other users.

it is necessary to deeply explore the user-generated content in social communication, especially to understand the actual opinion influence derived from the messages during the communication.


Each communication round starts after a user posts a tweet, and ends when the user updates her/his opinion with a new tweet.

we define the average number of active friends by "# of avg active friends".


To reduce the noise, we remove all the instances that fail to align.

we obtain 615,521 Chinese instances with tense information as the pseudo-labelled corpus.


Refer to Table 1, 73.1% of instances in the direction of Reason-Effect, and no instances in the direction of Effect-Reason are found in the Hypothetical, Inference, and Condition types.

only the performances of Purpose, Background, and Cause-Result are reported in Table 9.


On the other hand, they just appear in Topics 2 and 4 respectively.

they are document-indiscriminate words instead of topic-indiscriminate words, i.e.


Equation 3 shows that word t will have less probability to be assigned in topic k if the weight of word t is lower.

words with lower weights will get lower ranking in topics.


However, conventional neural attention models for entailment recognition problem treat sentences as sequences, ignoring the fact that sentences are formed from the bottom up with syntactic tree structures, which inherently associate with the semantic meanings.

using the tree structure of the sentences will be beneficial in inducing the entailment relations between parts of the two sentences, and then further improving the sentence-level entailment relation classification (Watanabe et al., 2012).


In fact these relationships could instead be utilized to not only better understand the underlying story but also generate resources for resource poor languages in question.

as a cursory step in understanding such hashtags, our work focuses on extracting multiword expressions (MWEs) from Twitter data streams.


Thus, we must also find ways to evaluate the extracted MWEs that involves minimal manual intervention.

the primary objectives of this work can be enumerated as: • Propose a new graph based method for MWE extraction that can circumvent the challenges of Twitter language usage, temporal nature of Hashtags and possible enumeration of all N-grams.


Further, there is even lack of availability of standard annotated corpora beyond a handful of languages (Solorio and Liu, 2008;Vyas et al., 2014) for code switched text and thus, almost little to no research even exists in extracting MWEs from such text.

at the very least we need to look at techniques that do not rely on POS tags.


In a multilingual scenario however, it is much easier to determine POS tags for a foreign phrase than to judge the compositionality or opaqueness of the MWE itself.

in continuation with the list provided by  that deals specifically with social media, we adopted an abridged version 1 as depicted in Table 1.


For a pair of vertices V i and V j input to the algorithms, it can be observed that the actual merge occurs on the vertices within the neighborhood of V i , V j and not on the vertices themselves.

in order to cover as much graph as quickly as possible, the easiest strategy is to pick V i , V j in decreasing order of degree.


Comparing against Table 2, the method seems to suffer for predominantly English datasets (low OOV% -Pride, Pres-identObama etc) but better for multilingual datasets (Brexit, PokemonGO).

we could in principle augment our method with either AMs or existing POS based approaches for English to further improve performance.


From the above example (apples⇢organisms), it is clear that during traversal of an upward generalization path, the correctness of individual edges is inconsequential to finding a good generalization for starting node (i.e., apples) once the first wrong edge (fruits↝vegetarians) is encountered.

a good taxonomy should not only provide a large proportion of correct edges, but also provide correct generalization paths, i.e., paths which are correct in their entirety.


To avoid bias, it is desirable that paths sampled from different taxonomies start from the same node.

wIBI C , which lacks the notion of entities, is first augmented with E→C edges from HEADS, resulting in a new hybrid taxonomy hereafter referred to as wIBI C +H E .


Unfortunately, the coherence assumption of the global approach does not hold in some situations, necessitating the discrete/coarse features in the local approach as a mechanism to compensate for the potential exceptions of the coherence assumption Hoffart et al., 2011;Sil et al., 2012;Durrett and Klein, 2014;Pershina et al., 2015).

the global approach is still subject to the second limitation of data sparseness of the local approach due to their use of discrete features.


Generalization Ground SAPs are limited to what has been directly observed in the corpus.

the elements of an SAP are generalized to their POS tags to increase recall.


Types are relevant to both textual and mathematical contexts of queries and documents.

types have potentially more impact than generic technical terms.


For example, both "monoid" and "semi group" have an underlying "set" and an associative binary operator.

mathematics text may coerce either structure to its carrier set in formal argumentation and make statements that are true for both structures (Ganesalingam, 2008).


our "document collection" is a collection of text chunks.


We experimented with the document structure attribute -title, the main text, or H1-H3 headingfor term weights and discovered that term pairs appear in these structure elements somewhat equally for relevant and non-relevant documents.

we do not use the document structure attribute.


(1) above still treats each label separately and cannot link y (n) and rev(y (n) ) to capture the relations between x (n) andx (n) .

we further construct a one-to-one correspondence relationship between (x (n) , y (n) ) and (x (n) , rev(y (n) )).


Because of the free word order and frequent omission of words in the Korean language, regularities are not easily found.

the rules do not pose many restrictions, as summarized in Table 4.


We apply the max and average pooling operations and find that the former performs better with less computational complexity.

we apply the max pooling operation to the output of convolutional layer which transform the feature map of size l We apply m kinds of matrix weight to get m feature maps.


Lemma overlap: Past works showed that lexical overlap contributes to determining topical alignment between two texts .

we compute lemma overlap of nouns, verbs and adjectives between two snippets.


We further wanted to measure the impact of our approach on a larger corpus, more suitable for machine learning tasks.

we extract from Debatepedia 5 pairs of snippets compliant with the structure and the content of the two other datasets.


Hindi is phonetically typed while English (Roman script) doesn't preserve phonetics in text.

along with diverse sentence construction, words in Hindi can have diverse variations when written online, which leads to large amount of tokens, as illustrated in Table 2.


We perform convolution of Q with a filter H ∈ R d×m of length m after which we add a bias and apply a non-linearity to obtain a feature map f ∈ R l−m+1 .

we can get sub-word level (morpheme-like) feature map.


Althoughc is a weighted context according to the aspect phrase, the aspect phrase p itself is still a necessary source of information for grouping.

we concatenatec and p to produce a vector x as the input to MLP.


Naturally, aspect phrases in some domains, such as MP3, may have fixed meanings.

an aspect phrase and its context have less correlation under the grouping task in these domains.


(2008) stress, relying on this kind of data causes the model portability to be difficult; a drawback that disappears when focusing on the content of the questions and answers only.

our model is based on textual content only.


However, the latter choice introduces a lot of false positives as sentences in relevant questions may be unrelated.

for correctly evaluating the sentence classifier, we used labels obtained by crowdsourcing.


Although, TF×IDF has a peak with 3 sentences, adding more pairs results in a quick performance decrease.

the supervised sentence classifier is more stable and thus preferable.


For example, in Figure 2, for a given relation, say Located in, the candidate entity pair is (LOC, LOC).

the two tasks are interdependent and optimising a single network for ER and RC to model the interdependencies in the candidate entity pairs and corresponding relations is achieved via the proposed joint modeling of subtasks and a simple piggybacking approach.


classification as a sentence-level multi-class classification, and rely on the relation arguments provided in the sentence.

they are incapable in handling multiple relation instances in a sentence and can not detect corresponding entity mention pairs participating in the relation detected.


We then compute a count vector C RE from these possible entity types.

u-LOC and L-LOC each with occurrence 2, while u-ORG and L-ORG each with occurrence 1 (Figure 7).


In pipeline approach, the only relation candidates are word pairs with (U-*, U-*),(L-*, L-*) or (U-*, L-*) entity types.

only w 1 and w 5 from word sequence are composed in M 2 for RC subtask.


Multi-task learning (Caruana, 1998) via neural networks (Zhang and Yeung, 2012;Seltzer and Droppo, 2013;Dong et al., 2015;Li and J, 2014;Collobert and Weston, 2008) have been used to model relationships among the correlated tasks.

we present a unified neural network based multi-task framework to model the entity-relation table for end-to-end relation extraction.


the avg representation) and hence are weak in capturing fine-grained complex linguistic phenomena.

we believe that modeling parallel sentences still remains a serious challenge.


Although these models are very useful for natural language processing (NLP) tasks, they only provide monolingual information which is not adequate for crosslingual NLP, MT, multilingual text classification etc.

some models have been proposed in this regard.


In the new model words are explicitly mixed together but the final result is not satisfactory.

the simple combination of words is not enough to boost the PBSMT engine and words should be processed efficiently.


We call it implication because the meanings or topics of the anecdote are hard to be read directly from the story itself, since the story is usually a factual description.

we have to infer the implication by means of extra information and strategies.


Stories are mainly used as evidence to support the thesis and main ideas proposed by the writer.

the thesis and main ideas could be viewed as the implications of the stories in the same essay.


The main ideas argument from multiple aspects.

we should link main ideas to nearby stories.


Relatedness measurement We are interested to study which information-the anecdote story or the anecdote implication-contributes more for measuring the true relevance.

we focus on comparing the following strategies for ranking anecdotes: • Query-Story (QS).


In other words, the performance of the frequent relations, due to their much higher frequencies, completely dominates the performance of the infrequent ones.

figure 6(b) shows a corresponding graph aggregated across only the top-8 infrequent relations.


Like in Figure 6(a) before, the performance of the frequent relations, due to their superior frequencies, completely dominates the performance of the infrequent ones.

figure 9(b) shows a corresponding graph aggregated across only the top-8 infrequent relations.


For example, in Figure 2(b), sentence 3 is the left child of the node (3->4, Contingency) and sentence 5 is the right child.

we infer the discourse relation between 3 and 5 as (3->5, Contingency).


The intuition behind incorporating pre-trained vectors is that they might help the model recognize bridging references of those involving synonyms (e.g., a house ... the home).

we compare the following options: • Random: The embedding is initialized randomly.


Our model does not rely on task-specific features, only task-specific training.

the exact same model should be applicable to other tasks involving semantic and pragmatic knowledge.


Moreover, some prior intuition is required to incorporate non-local context.

inspired by previous research on multi-stage prediction 1 , we propose a two-level model for aspect-based analysis.


Using this, at second level, we sample important non-local neighbor units to embed non-local context into input features.

such sampling is not necessarily restricted to presence of coreference or discourse.


Henceforth, these will be referred to as terminal labels.

a group G contains information about the cluster of units as,  additive and, or, also, therefore, furthermore, consequently, thus, as a result, hence, subsequently, eventually, in addition, additionally, moreover, as well as Contrast though, although, however, but, despite, yet, still, nonetheless, nevertheless, in spite G (polarity; count; distance) polarity sequence in Example 1 can be reduced to this form as G(P;3;1)-G(X;3;0)-G(N;1;1)-G(O;2;2).


(2014).Moreover, a unit may contain multiple sentiment terms.

the scores are aggregated and normalized.


However, prediction is made over full sequence of output labels and features F(U 1 ) to F(U N ) are fed together.

the grouping is done only once unlike that described in Figure 5.


Aspect-categories are not provided in Bing Liu's data.

the category related baseline features are dropped for this data.


On top of the base model, the combined 'Base + Level-2' model shows higher accuracy scores.

embedding non-local neighbor features does provide richer context information, thereby also resolving sentiments associated with ambiguous sentences or phrases.


In Rhetorical Structure Theory, much intricate discourse relations have been proposed and there have been interesting works in this area exploring richer discourse concepts.

in further work, we would expand the discourse relations used in sentiment aggregation as well as for linking aspect-units.


Basically, the transition probability between the source text document and its corresponding response text document can be set to 1, assuming that they exhibit the same category.

the transition probability matrix  Where I is an identity matrix of dimension n, containing ones along the diagonal and zeros in all other positions; s M and r M are the transition probability matrixes, calculated using formula (1) in the subgraphs, s G and r G , respectively.


This means that, with the exception of obvious affect keywords, other lexical or co-occurrence elements in a statistical model have little predictive value individually.

statistical text classifiers only work with acceptable accuracy when given a sufficiently large text input.


For example, the concept "hot dog" is very different from the concept "dog".

we make each category of the dataset contain 10 multiple-word entities and 20 single-word entities.


The advantage of NCE is that it allows us to fit models that are not explicitly normalized making the training time effectively independent of the vocabulary size.

we will be able to drop the normalization factor from the above equation, and simply use e xw•vw in place of P (x w |v w ).


In contrast, embedding information is less sensitive to the effect of word frequency.

tWE-1, LFDMM and LtE demonstrate better performance than LDA when the number of topical words varies from 3 to 10.


More concretely, the matrices Q c and S are initialized with I and E, which are identity matrices, and the Lipschitz constant: The underlying idea for reducing noise is to make use of a filter f to learn a denoising matrix Q c ; hence, we design the filter f as a non-linear, parameterized, feed-forward architecture with a fixed depth that can be trained to approximate f (X, Q c , S) to X as in Figure 2a.

noise from word embeddings will be filtered by layers of the filter f .


The sparsity of Z depends on the 1 regularization λ in Equation 7; and the length vector K of Z is implied as K = γL.

we aim to tune λ and γ such that Z represents the nearest approximation of the original vector representation X.


We demonstrate the effectiveness of the novel approach by including information from WordNet.

we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.


According to the distributional hypothesis, words with similar context features have a similar meaning.

we can use any distance measure between the feature vectors as a measure of semantic similarity.


report only the result for one arbitrary split into test and training set.

their results cannot be compared directly to our and also Bullinaria and Levy results.


An obvious alternative is an ensemble classifier, that uses the results of the classifiers using only one type of information.

we also trained an SVM on the results of the SVMs using WordNet and distributional features.


In the first sentence, the preposition "in" is translated into the French preposition "dans", whereas in the second one, it is translated into the French preposition "en".

a representation that is predictive of the preposition's translation is likely to be predictive also of its sense.


With the emergence of the social web, a large part of our daily communication has moved online.

the past decade has seen an increased research interest in text mining on social media data.


(2013b), word vectors obtained from deep learning neural net models exhibit linguistic regularities, such as additive compositionality.

σ vn is justified to predict surrounding words of the composition of, say, blow and whistle in the literal usage of blow whistle.


Here we assume that σ vn is normalized to have unit length.

p i = v t i σ vn indicates how strongly word vector v i is associated with σ vn .


The assumption is that a word vector is learned in such a way that it best predicts its surrounding words in a sentence or a document (Mikolov et al., 2013a;Mikolov et al., 2013b).

the words associated with a literal target will have larger projection onto a target σ vn .


C-forms are fixed forms corresponding to the syntactic patterns in which the idiom normally occurs.

the gold-standard is "noisy" in CONTEXT.


cONTEXT+ is a supervised version of cONTEXT.


The resulting vocabulary has 71,290 words, each of which is represented by a q = 200 dimension vector.

this 200 dimensional vector space provides a basis for our experiments.


These large-sized models are not only inefficient in terms of network bandwidth, but also significantly increase energy consumption (Han et al., 2016).

it is vital to devise a method that achieves as small a model as possible.


Combining these results implies that, if we carefully choose informative instances for training, the distortion in the reduced space is only affected by log log m, which should be negligible.

we expect the theorem to virtually hold even in this setting.


4, there are 163 words in the WL wordlist which map to more than 500 words in the reverse map.

the distances of multiple words to the input words are similar, obscuring the semantic content of the similarity measure.


An exhaustive comparison will deviate from the goal of this paper, which is to show that at least some visual embeddings are able to better represent certain attributes than state-of-the-art DMs.

we employ an off-the-shelf CNN model, as CNNs generally outperform the old approaches such as SIFT, HOG or SURF (LeCun et al., 2015).


For completeness, we have repeated our experiments with word2vec embeddings (Mikolov et al., 2013) and we have found GloVe to perform slightly better.

we report results with GloVe as it provides a stronger baseline to compare visual representations with.


The use of 5 folds is convenient since the data set is small and contains just a few instances of the minority class-which ranges from 6.25% to 30.5% of the data.

the use of 4/5-th of the data for learning (and 1/5-th for testing) is more likely to yield well-learned attributes than smaller training proportions.


In this case, the bottleneck might be any sort of technical issue such as our classifier choice, regularizer choice, data scaling, etc.

in order to validate our conclusions, we repeated the same experiments with different classifier choices (SVM, logistic regression, bagging ensemble of decision trees and AdaBoost); different regressors (SVM, neural networks, ensemble of regression trees and gradient boosting); and data scalings (max/min scaling and component-wise centering plus normalizing by the standard deviation).


Taken together, we conclude that fine-grain attribute understanding can benefit from the integration of visual and textual representations.

our results align with previous multimodal research that evaluates vision and language in coarse-grain tasks such as concept similarity and conclude that multimodal representations outperform the unimodal baselines (Lazaridou et al., 2015;Silberer and Lapata, 2014;Kiela and Bottou, 2014).


As context-PASs, we used a non-negated transitive verb, intransitive verb, adjective, and copula.

we do not extract tuples including one or more negations (e.g., ⟨John, not eat, apple⟩).


It may be presumed that this improvement is due to the task setting: pronominal coreferent clusters are relatively difficult for SP to discriminate because SP cannot exploit contextual information.

we also report MQ no pr in Table 2, which is the MQ of the coreference cluster ranking task including only nonpronominal nouns as candidate coreference clusters.


Although CSP outperformed SP for two tasks, the superiority of CSP may be argued to be due to the larger number of training instances; SP is trained on A pro but CSP is trained on both A pro and B pro .

we plotted the learning curves (SP and CSP in Figure 4), trained SP and CSP on subsets of A pro and B pro sampled randomly, and measured MQs on the models.


The main drawback of this data set is its restriction on non-derived adjectives, which are relatively rare in German.

the data set is fairly small, and it contains several outdated and rarely used adjectives.


Clusters are built by constructing the transitive closures.

if d ij and d ik belong to the 5% lowest distances measured, the words belonging to the rows r i , r j and r k are clustered together.


For instance, the substitution of u for v would not work for sentences that have negations or quantifiers such as 'all' and 'none'.

one cannot replace 'cat' with 'animal' in sentences such as 'all cats are asleep' or 'a cat is not asleep'.


Unlike English, subject ellipses frequently occur in Japanese.


Intuitively, when a human is judging the relationship of the two sentences, he/she would first read the premise, and then read the hypothesis while considering whether it can be entailed by the premise.

we intend to make the model more like human, namely, we require the model be capable of reading and thinking.


In this paper, we propose a new LSTM variant called re-read LSTM unit (rLSTM), which also take the attention vector of one sentence as an inner state while reading the other sentence.

this kind of unit is specially designed for dual sentence modeling.


The rLSTM unit is specially designed for dual sentence modeling, it is applied only when dealing with the second sentence.

in Figure 1b, there is a general input to rLSTM: the premise representation.


The model needs to consider the entailment relationship.

the memory of the premise should also affect the hidden state of the hypothesis.


So the A t−1 is the premise's attention in time step t − 1, after read one word, it became A t , the information represented by which is more focus on the relationship between the premise and the hypothesis.

the origin LSTM's memory cell can affect the attention in time step t: where are weight matrices or vectors.


in Figure 3e and Figure 3f, the model's attention performs very poor.

the test accuracy of tLSTM−C info is much lower than tLSTM.


Otherwise, if none of the two words is included in any of the synonym or hypernym sets of the other, then we use the sum of Jaccard coefficients of the above-computed overlap, of the combination of the given words and stemmed words, as the value of the best word overlap feature.

for the four different POS tags we consider, namely noun, verb, adjective and adverb, this gives us a set of four different features.


Combining different words from the input sentences creates several intersections -each one as valid as the other.

instead of defining sentence intersection as a single sentence that contains all the information shared by the input sentences, we define it as the set of all such sentences.


Whereas ROUGE tries to maximize agreement with all gold summaries, we are satisfied if there exists even one gold intersection sentence that is sufficiently similar to the given candidate sentence.

to calculate the precision of a given candidate intersection sentence c ∈ C, we take the best-matching gold sentence g, and calculate the portion of n-grams in c that are covered by g. Similarly, a gold sentence's recall is measured versus the best-matching c ∈ C. We define an intersection set's precision and recall by taking the averages.


We observe that for certain dependency types 4 that connect function words to content words as modifiers, the modifiers should always be part of the same alignment as their heads.

if the head participates in an alignment, the modifier is added as well; e.g.


In most NLP tasks, LSTM outperforms vanilla RNN (Sundermeyer et al., 2012).

for our model we only explore LSTM as a basic unit in the encoder and decoder.


For example, these metrics do not reward the paraphrasing of "London" to "Capital of UK".

we also evaluate our models on a sentence similarity metric 4 proposed by Rus et al.


p-value of these tests are less than 0.05 in all cases.

comparison between two models is significant at 95% CI if the difference in their score is more than the variance due to test set selection (Table 4).


This stems from the fact that majority of entities are names, proper nouns and named entities which are not well covered in existing translator.

we use a heuristic method to handle the entities not covered by Bing translator.


However, most documents do not provide keyphrases, and the daily flow of new documents makes the manual keyphrase annotation impractical.

automatic keyphrase annotation has received special attention in the NLP community and many methods have been proposed (Hasan and Ng, 2014).


Due to this limit, we argue that the stance of a tweet is not independent of the social context in which it was generated.

for accurate predictions these social behaviors must also be modeled.


In reaction to this, Republicans will tweet about protecting American citizens' rights to gun ownership, which falls under the Constitutionality frame.

we expect similarities and differences in framing around events to indicate similarities and differences in stances and agreement patterns.


A word "story" could be a domain-independent feature for Books and DVD but also could be a domain-dependent feature for Books and Kitchen.

suppressing domain-dependent features for multiple domains which works better in the previous task could be the reason that limits our model on this unsupervised domain adaptation task.


Unfortunately, any reasonable restriction on the run time of the sampler means that we will only be able to explore a localized area of the support.

for most uses of probabilistic models of text, practitioners run a sampler for a period of time in the hope of finding an area of high probability, and then use the final sample as an approximation for the mode.


Since the data x is fixed, the posterior is proportional to the joint distribution over all of the variables and data: Using the chain rule, for some i we can then factor the joint distribution as: Since x is fixed, we can also write While updating the parameter θ i , θ ¬i is held fixed, which means that the second term p(θ ¬i |x) is constant and the factored joint distribution is proportional to the complete conditional, thus: Since these two expressions are proportional, setting θ i to the mode of its complete conditional will only increase the value of the posterior probability.

our update rule for the variable θ i is given as: Since this update equation monotonically increases the estimate of the posterior probability, and the value of the posterior probability is bounded above by 1, we can use the monotone convergence theorem to conclude that this algorithm will converge to a local maximum in the posterior distribution.


To be clear, Iterated Conditional Modes is a coordinate ascent algorithm, and as such, it can only locally optimize the posterior -there is no guarantee of finding a global maximum.

if the posterior manifold contains many sub-par local maxima, then ICM will be sensitive to initialization and may perform poorly.


Due to these run time constraints, it has been argued that traditional document clustering techniques may not work out of the box (Carpineto et al., 2009b).

various specialized algorithms for the problem of web search result clustering have been published.


However, such optimization tends to undo the constraints, rendering the model useless (Hu et al., 2011).

we use the published Gibbs sampler as our baseline inference algorithm.


This falls well within the run time recommended by Cook and Thomas (2005) for complex user-initiated tasks such as rerunning inference on MOMRESP given additional annotations.

if MOMRESP were to be adapted for an active learning task, then ICM would provide not only accurate posterior inference, but run times which are amenable to active learning.


The underlying assumption is that those features generated by a user during the past day are most likely to have influenced her mood, resulting in the observed mood scores.

given a mood form completed by a certain user at time t, we focused on her past 24 hours before t, in order to extract our features from and aggregate these features in different time windows within the 24 hour period, and, more specifically, into 5 different windows (1, 6, 12, 18 and 24 hours before the completion of a mood form), to allow for an extra level of granularity to the effect of proximity to the mood form timestamp.


Making predictions in this formulation requires an inference procedure over g, that finds the bestŷ for a given x.

the prediction function h is, h(x; w) = arg maxŷ ∈Y g(x, y; w).


Apart from the ethical issues raised by these platforms (detailed in (Fort et al., 2011)), microworking platforms do not support true training of the workers: tests can be set up to select them, but they have to perform hidden work to train themselves (as shown in (Gupta et al., 2014)).

tasks must be simplified to be manageable by workers: for example, a task related to recognition of textual entailment across several sentences (which might actually reflect entailment, neutrality, or contradiction) could be simplified by presenting only one pair of sentences and a binary response to a single question, such as "Would most people say that if the first sentence is true, then the second sentence must be true?"


Since the analysis of a whole sentence is complex, 15 we decided to decompose it: the full annotation of a sentence is split into atomic tasks, where each task is linked to one type of dependency relation.

the player is trained on one type of relation at a time, instead of twenty, and must focus on only one type of dependency relation across several sentences.


When a corpus is exported, for each token (lexical unit), we consider all the annotations in the database for which it is a dependent element and select the one with the highest score.

each token receives exactly one governor with one relation and we can ensure that the exported corpus contains well-formed dependency trees.


Hence, similar words or synonyms will have closer word vectors (measured by cosine distance).

for the purpose of sentiment polarity classification (with no intensity segregation), all synonyms will contribute to the same polarity in a similar manner.


For example, "zhǒng" can be translated into "type" or "kind", and "bùmǎn" also has two candidate translations "opposition" and "dissatisfaction".

during translation, the decoder has many candidate translations for the same source phrase, which we argue is one of the reasons for our success.


We found the indicators to be helpful also when no adaptation is performed, possibly because it allows a form of multitask learning over the different documents and systems.

we use indicator features for both aggregation strategies.


Predicting Deviation from the Document-Mean: Our active selection strategy only makes use of relative differences between segment scores.

at training time, we replace the label y ji for the i-th segment of the j-th document (system output) by y ji − Q is the true overall quality of the j-th document.


Although the tagger learns 41 semantic categories, we included (B) beginning and (I) continuation as supersense prefixes to learn more categories.

actual label space to be learned increases to 83 (including "NULL").


For example, in our earlier human evaluation of HTER, it would have been possible to allocate resources to assessment of the fluency of translations, in addition to adequacy, but this would have come at the cost of reducing the size of the test set by approximately one half.

Figure 2: Significance test results (Williams test) of statistical significance of a difference in dependent correlations with human assessment for competing out-of-English human-targeted metrics; a green cell denotes a significant increase in correlation with human assessment for the metric in a given row over the metric in a given column at p < 0.05. the decision to include fluency should be weighed against the degree to which reference bias is actually present in the adequacy evaluation.


Their conclusion was based on estimation of confidence intervals for Kappa coefficients by means of an unconventional resampling technique, where samples used to estimate confidence intervals were smaller than the original sample size, drawn without replacement, and averaged; this diverges from standard methods of confidence interval estimation, such as bootstrap resampling.

the reported confidence limits are unreliable, bringing their conclusions into question.


To complicate things further, since nonnative language skill levels will vary considerably from one human assessor to another, the degree of bias is likely to change considerably depending on particular language ability levels.

in addition to the potential of monolingual reference bias, we point out the real possibility in the case of DA src of bias caused by reliance on bilingual human assessors not native in either the source or the target language.


That is, it is possible that a sentence is overall out-of-domain, although part of it can be in-domain.

a few works consider more granular level for selection.


We use F j for the variable associated with the source word occupying position j in the source sentence f m 1 .

a j is the random variable for the alignment link associated with position j.


It can be shown that, with conditionals as shown in (9), the transition kernel underlying this Gibbs sampling procedure is Harris ergodic (Neal, 2003).

the procedure is not only correct but also very efficient as we only sample from uniform distributions.


In contrast to manually encoding rules for language pairs, we could use similarities and differences between target languages encoded in existing typological databases of structural properties of the world's languages, e.g., the World Atlas of Language Structures, WALS (Dryer and Haspelmath, 2013).

the challenge taken up in the present work is how to exploit typological databases such as WALS to guide the learning algorithm into making the right decisions about word order.


While the experiments using the subtitle corpora presented in the previous section allow a fair comparison of a large number of language pairs, they also exhibit certain restrictions: (1) all experiments are limited to a single domain, (2) the source sentences are fairly short, and (3) to ensure consistent corpus sizes, a limited number of 800k sentence pairs had to be used.

we perform an additional set of experiments with data from different domains, longer sentences and a larger number of sentence pairs.


We will discuss such properties of German in Section 3.

instead of using words as only inputs and outputs, there have been several approaches which take into account more fine-grained units such as characters.


For all sentence pairs (S, T ): ∀s ∈ S, t ∈ T : The new conditional probabilities are calculated in the maximization step and for making means and covariances for the source words, these probabilities are used as weights for weighted averaging of the vectors: In the initialization of the EM algorithm, for all pairs of words, the probability p(t|s) is equal to 1.

the distributions of source words are made based on their co-occurrences with the target words, which is a similar situation to initialization of the IBM model 1.


As can be seen from the Figure 1, the proposed vector model has better performance during all iterations of the training and it seems that 5 iterations for training is suitable for both vector model and the IBM model.

for the rest of the experiments, the models will train for 5 iterations.


FSD is a very challenging, if not impossible, task for human operators, since it requires inspecting millions of documents per day.

accurate, automatic solutions are very desirable.


In this methodology, the selection of an appropriate similarity metric and the selection of an informative document representation are essential.

many different metrics have been studied in the literature, such as the cosine similarity (Petrović et al., 2012), the KL-Divergence (Karkali et al., 2013), and the named entities overlap (Kumaran and Allan, 2004).


For example, the relations r 1 = (A, proposes, X) and r 2 = (A, suggests, X) have probably the same semantic meaning.

we decided to exploit Word2Vec, a technique described in (Mikolov et al., 2013) that learns a vector representation for words.


However, it is unclear how to compare two documents using their relations.

we propose Relation Similarity (RelSim), a metric that compares semantically two documents using their named entities and relations.


In order to be fair to all test takers, such forms need to be comparable in terms of difficulty of the items they contain.

item difficulty is a crucial parameter for every new item added to the test.


Moreover, many studies explored individual features.

syntactic complexity and vocabulary difficulty features have been found to have a strong and consistent effect on reading and listening comprehension (Blau, 1990;Nissan et al., 1995).


Total   where p is the proportion of the test taker population who answered the item correctly and Z p is the Z value corresponding to a given p value in the standard normal distribution (Holland and Thayer, 1985).

on the delta scale, the higher delta values denote more difficult items.


All classifiers produced similar results for the benchmark system based on item sequence.

for consistency we used random forest regressor for both benchmark and complexity-based models.


C and M items infer the test takers' understanding of the listening passage.

we initially hypothesized that the text complexity of a common listening passage would be a strong predictor of the item difficulty.


However, they also addressed that the regular expression patterns faced challenges in long-distance collocation extraction and considerable risk of mis-classification, which is more serious in Chinese than in English.

a better strategy should incorporate richer annotation of corpus and take more consideration of Chinese grammatical features.


We also find that over 86% of these mistakes occur only one time, and over 8% of them occur two times.

oCCA now only outputs collocations with occurrences no less than three times to reduce the negative effect.


For instance, the rich sets of features that are typically used to solve the four tasks in the IE pipeline for event coreference, are ill-suited for modeling as explicit MLN formulas, since they will yield a large MLN having millions of features.

a major contribution of our work lies in the proposal of a novel hybrid approach where we embed such features as weighted unit clauses in a low-dimensional space, and then integrate these clauses with the rest of the MLN formulas during inference.


Even though these methods can achieve fair performance in small datasets, they have either of the following limitations: • No time-awareness: many event detection models do not take into account time information.

it is very likely that the documents that talk about different events (as Table 1 shows) are grouped into one cluster just because their lexical similarity is high.


For a node v, its PageRank value pr(v) is computed as follows: where |V | is the number of nodes in the BINet, N (v) denotes the set of nodes adjacent to v, d is the damping factor and is set to 0.85, , which is the normalized weight of the edge between v Intuitively, a node with a high PageRank value is usually important and likely to be the key node that indicates the key information of an event.

we rank nodes in the BINet by their PageRank value and choose the node which has the highest PageRank value and does not belong to any community as a key node to construct a community E around it with its closely related nodes: where v is the node with the highest PageRank value and does not belong to any community,ω v,u is the normalized weight of the edge between v and u, and σ N is the threshold for selecting closely related nodes.


In TDT4 dataset, hardly can we see multiple events of the same type in the TDT4 dataset (e.g., there is only one flood event in TDT4 dataset).

even if we just use content-based clustering methods regardless of time information, the performance is not bad.


However, corpus fusion for emotion classification is challenging due to the following two factors: First, the emotion taxonomies are often different between two emotion corpora because of the lack of an accepted standard.

similar instances which express similar or same emotion can be categorized into different types of emotions under different taxonomies.


It means that we align happiness under the taxonomy of YAO (2014) to both joy and positive under the taxonomy of HUANG (2015) together with the alignment constraint defined below.

the cost vector C H changes to: the objective function becomes to: Subject to: where formula 14and 15unify the number of possible labels on both taxonomies to one because we don't consider any intra-corpus constraints which are derived from the annotation guideline of YAO (2014) when extra-corpus is solely applied.


Different context words have different influences on determining the sentiment polarity of a sentence towards the target.

it is desirable to integrate the connections between target word and context words when building a learning system.


Target-dependent sentiment classification is typically regarded as a kind of text classification problem in literature.

standard text classification approach such as feature-based Supported Vector Machine (Pang et al., 2002;Jiang et al., 2011) can be naturally employed to build a sentiment classifier.


Clearly, as mentioned in the beginning, the design of the relations by Mann and Thompson (1988) suggests that the theory be especially amenable to argumentative text.

it can be expected that a structural analysis in terms of semantic and pragmatic relations, equipped with nuclearity, might capture what we are characterizing as depth of argumentation here.


The central idea in the present paper is to exploit word dependencies as features for event detection.

the development of the model to predict dependencies for a given video is a key issue.


Different from the existing works, this representation encodes the relationship between concepts based on the syntactic dependencies between words.

it captures semantically richer information, which is crucial for video event detection.


Intuitively, if one is interested in some areas, it is natural that he or she will follow some related celebrities on social media.

celebrities that one follows can reflect the interest of the user (Lim and Datta, 2012).


Generally speaking, elder people have higher spending power.

topics that elder users prefer are positively correlated with spending, while most topics that younger users prefer are negatively correlated with spending.


(2011) uses Wikipedia article titles as candidate labels, which is hard to assign correct labels for the new emerging topics in Twitter because there may not be the timely articles about the new emerging topics in Wikipedia.

despite the success of these works, they are either time-consuming or hard to assign labels for new emerging topics.


Intuitively, when an event occurs, the information about the event exists in all kinds of sources (labeled data and unlabeled data), such as news, forums and social networks.

these sources are parallel information channels.


Meanwhile manually generated labels require lots of human effort to generate such labels, and can easily be biased towards the user's subjective opinions.

several automatical labeling methods have been proposed recently.


Despite the success of these works, they are either time-consuming or hard to assign labels for new emerging topics.

in this paper, we will focus on the problems above, and proposed our fast topic labeling framework.


denotes the contribution of document d to the lower bound of Formula (1).

this reveals that the variational objective relies only on n dw , the number of times word w appears in document d. an online inference algorithm for Labeled LDA can be derived.


However, OLLDA only processes the new coming document and update parameters.

as Figure 2 shows, BLLDA costs much more time to get the new parameters compared with OLLDA, especially when the number of observed documents is large.


That means: the point p 0 locates in the bound of S 1 and S 2 , however, the set for Euclidean-based methods is subset of the one for angle-based methods, which means that the ability of finding KNN points for angle-based methods are better than the one for Euclidean-based methods in probability distributions.

f R sh,1 always performs better than f R ps,1 .


Small ratio means that a lable has fewer points in a probability vector set, thus it's natural that the values of all metrics are very low.

we evaluate all ranking results judged manually by three students, and take use of voting method to obtain the final results, showed in Table 6.


The improvements are significant by t-test at the 95% significance level.

the overall results show that proposed distribution similarity ranking is effective.


This can minimize the number of dialogue turns and lead to more efficient exchanges.

we predict that effective teams would make more disfluencies due to the need to plan and coordinate at higher speeds.


However, it has been observed that table contents are also present in the chunk.

we use a feature "bold font" to extract bold tokens from such chunks.


(2013), GROBID provided the best results over seven existing systems, with several metadata recognized with over 90% precision and recall.

we compare OCR++ with the state-of-the-art GROBID.


While for the yat variable the point visualisation was already informative, due to a strong spatial trend as reported in Table 2, for theštošta variable, showing a weaker spatial trend, the visual identification of regionally dominant levels turns out to be considerably difficult.

the dominance map comes in very handy, showing that only in central Croatia thešto level is dominant while in the rest of the study areašta dominates.


While the value of WordNet for NLP is indisputable, it is generally recognized that enriching it with additional information makes it an even more valuable resource.

there is a line of research aimed at extending it with novel terminology (Jurgens and Pilehvar, 2016), cross-predicate relations (Lopez de la Calle et al., 2016), and so forth.


(2013b) in a word-level machine translation task.

we release an extension of WordNet with detailed collocational information, named ColWordNet (CWN).


Nevertheless, the agreement between the four annotators for all types was substantial with κ = 0.606.

we decided not to modify our scheme, but only to clarify the type definitions and to add some additional examples that clarify these hard cases.


Considering that the annotators had to decide among eight different classes for every segment, such agreement seems high in overall terms.

we conclude that the annotations of the corpus can be seen as reliable.


Moreover, words are often further inflected after derivation, or may be multiply derived, and the analysis of these cascading and overlapping suffixes is an ambiguous and unreliable process.

instead of derivational morphemes, the minor part-of-speech tags assigned to each word (given in the POSTAG column) were used to identify derivations.


In the case of EGY, the sizes of the analyzers are 5K, 15K, 45K and 135K, while the sizes of the LEV analyzers are 5K, 15K and 45K.

we can evaluate the performance of the EGY and LEV analyzers at different sizes and compare them.


This lexicon-level error has a significant impact on the generated Proposition Bank since every TL sentence with this annotation is incorrectly labeled.

as the middle pane in Figure 1 illustrates, we wish to filter out such lexicon-level errors.


This lexicon-level error causes inconsistent annotation of the same semantics throughout the generated Proposition Bank.

as the right pane in Figure 1 illustrates, we wish to merge these two entries into a single entry comprising both frames.


Schuster and Paliwal (1997) proposed BLSTM to extend the unidirectional LSTM by introducing a second hidden layer, where the hidden to hidden connections flow in opposite temporal order.

the model is able to exploit information from both the past and the future.


On the one hand, we are interested in the effect of language similarity on bilingual word embeddings in a (Q1) monolingual (intrinsic) semantic evaluation task.

our first question is: how does the performance of bilingual word embeddings The (idealized) goal of crosslingual embeddings is to capture these relationships across two or more languages.


LRCos is not completely free from this negative effect, but it suffers less as the effect is mitigated by the fact that regression can assign near-zero weights to the dimensions which are not responsible for the target analogical relation.

algorithm performance continues to grow with larger vector sizes.


Figure 1 shows an example sentence from a Russian fairytale which contains three animate chains, one of which is a tree that talks: trees would not be normally be considered animate according to canonical lists of animate entities.

some context sensitivity in detection is needed.


Orǎsan and Evans (2007) did their work in English but their data was not available.

we sought other data (specifically stories, because of our interest in story understanding), and our annotated data was a corpus comprising a variety of Russian folktales, Islamist Extremist stories, and Islamic Hadiths that are freely available and assembled for other work, and had been annotated for referring expressions and coreference chains Finlayson et al., 2014).


We checked whether a given referring expression contained a noun and encoded this as a boolean feature because we observed that in the first 15 stories 43% of nouns are animate.

this feature explicitly captures the tendency of nouns to refer to animate entities.


2017, we completed the annotation for the first two seasons and further annotated two more seasons.

the first four seasons are completely annotated for character identification in our corpus.


A subset of the disagreed annotation was manually adjudicated by experts, from which we found that taking the union of the entity sets annotated by two workers would effectively give the correct set of entities for each of those disagreed plural mentions.

a vast amount of plural mentions were pseudo-adjudicated by taking their unions of double-annotation.


Since they take only singular mentions, a pseudo-singular dataset is created where exactly one entity is chosen for each plural mention based on the closest matching previous speaker or if there is none, chosen randomly.

the models trained on this pseudo-singular dataset always predicts one entity per mention.


Note our CEAF φ4 recall score is also 33% higher than CEAF φ4 precision score, which is related to their definitions.

our system identifies a lot of small subsets of entities, but may miss some crucial links in between.


Seeing that the number of documents with at least one positive class is much less common compared to the number of documents without any positive class (see Table 1), simply taking a randomly sampled document from the unlabeled documents will likely give a document with no class, which is less useful compared to document with at least one positive class.

we opt for a simpler method of asking annotators to make a binary decision on a   ).


In Section 6.1 we have shown how the document-level aggregation strategy may significantly affect the final result.

exploring ways to effectively select the thresholds might be worthwhile.


• Second, trained with the original MLE procedure, HA relies on gold action sequences computed by a separate character aligner.


Finally, we would like the model to be less dependent on the gold actions generated by the aligner, which is uninformed of the downstream task, and that at training, the model can choose alternative action sequences leading to correct predictions, if that helps it generalize.

to address all these issues at once, we train the model by minimizing the expected risk (Och, 2003;Smith and Eisner, 2006) of the actual training data t = {(x (l) , y (l) )} N l=1 : where y is computed from a and x, and the risk is given by a combination of normalized Levenshtein distance (NLD) and accuracy: an action sequence a attains the lowest risk of −1 if its corresponding output sequence y is identical to y (l) of the training sample and the highest risk of +1 if the number of edits from y to y (l) equals the maximum of their lengths.


Then, we combine the information in the obtained sequence m i,1 , ..., m i,n in a similar manner as in the ATT-POOL model using the attention mechanism, but this time, with self-attention, that is, computing the weights of the elements in the sequence based on the relatedness to the element inside the sequence.

the number of attentions that must be computed is reduced to nq.


We extended the end-to-end style model using a deep bi-RNN and introduced the components that consider the multiple predicate interactions into the input and last layers.

we achieved a new state-of-the-art accuracy on the standard benchmark data.


Due to limited funding, a full manual quality check is often not feasible.

methods that are able to detect errors in automatically predicted parse trees are of great value for data clean-up and might also be of use for domain adaptation settings and when dealing with low-resource languages.


The annotation task, however, is not easy due to the non-canonicity of the web data which includes many ungrammatical structures and does not conform to standard spelling conventions.

we asked a second trained linguist to adjudicate the disagreements between the original treebank annotations and the modifications carried out by the first annotator, using MaltEval (Nilsson and Nivre, 2008) to visualise the trees.


Surdeanu and Manning (2010) emphasize that the most important success criterion for ensemble parsing is not the complexity of the model but the diversity of the baseline parsers.

it is conceivable that we could get similar results using a simpler model, based merely on the predictions of the parsers without the variational inference model.


Similar observations can be found when replacing sr [i, j] with v[i, j] for BiaffineRule.

we use v[i, j] for the span models and sr [i, j] for the rule models in latter experiments.


they contrast minimally different inputs within the same language model.

any imbalance in the training data should not be expected to make a large difference in the global results.


Grammaticality is typically judged relative to a (grammatical) point of comparison.

who did a portrait that showed scared Tim?


As a supplement, it is useful to measure a probabilistic loss, where there is a cost associated with how correct the decision was.

we move beyond pure predictive performance and consider the actual probability distributions created by our models under different conditions.


Our agent interleaves actions that navigate in the document, with an action that runs an RC model in a certain document position and extracts an answer.

the agent can decide to continue navigation after extracting a certain answer.


Moreover, we would like to consider the overall document length, rewarding successful navigations in long documents.

we define the following reward: where u is the node where the agent is located and u * is the closest tree node that contains the answer (n(u) is the node index as defined above).


Therefore, we define the following reward: where u is the node where the agent is located and u * is the closest tree node that contains the answer (n(u) is the node index as defined above).

when stopping, the reward is proportional to the distance to the closest answer location given the document length.


By the knowledge acquisition, for a query event, we can obtain N (N =n1 × n2 × n3) reference samples in TERB.

given a pair of query events q 1 and q 2 , we obtain a collection S of pairs of reference samples (in the size of N × N ).


We implement CMSM between a scene (image) and a mention as the semantic approximation calculation between the caption of the image and the mention.

it can be boiled down to a text matching problem.


Evaluation-The methods we concern are evaluated by the metrics of macro-average Precision (Mac-P), Recall (Mac-R) and F-score (Mac-F).

for a particular type t, the positive examples are defined as the event pairs that hold the relation t. for t, the precision score is calculated as the ratio of the positive examples in all the output examples of type t. The recall is defined as the ratio of the output positive examples in all the ground-truth ones.


This makes it difficult to develop Holmes with a highlevel confidence.

the two types aren't taken into consideration too.


Therefore, the two types aren't taken into consideration too.

the total number of available instances in ACE-R2 is 1,974.


In particular, we excluded documents with fewer than five incoming links from other documents if the corresponding entity of the document is not contained in the dataset used in our fine-grained entity typing experiments, presented in Section 3.1.

the number of target documents was 702,388.


We also modified all words to lowercase, and excluded words that make fewer than five appearances and contextual entities that make fewer than three appearances in the documents.

the final dataset contained 242,771 unique words and 327,263 unique contextual entities.


Developing models that generalize across domains without further fine tuning generally entails modeling linguistic cues that are stable across domains.

1 in this work, we propose the first set of models that effectively generalize across domains by leveraging insights into the temporal usage of language.


4 While NEOPROBBAYES is better for large documents, it is quite sensitive to errors in estimates for small documents.

nEOPROBMEDIAn and nEOPROBMEAn which are less sensitive in the presence of outliers are better than nEOPROBBAYES for short documents (100 tokens) but are outperformed by nEOPROBBAYES for larger documents (2000 tokens) when individual model estimates are much more accurate.


In the math problem in Figure 3a, for the target number "30", Seq2SeqAttn + Copy model has a higher attention weight of the source number "0.75" than the source number "30".

the model wrongly aligned the target number "30" to the source number "0.75".


The rationale states that a good hub page points to many other pages, where a good authority page is pointed to by many hubs.

the two scores are computed dependently on each other.


However, since our task is slightly different in that we want to generate keyphrases for overall trend detection of an area (as opposed to the more typical characterization of a single publication), we need to introduce several refinements.

we propose our multigraph ranking (MGR) component for salient keyphrase extraction.


Subsequently, each node from {coherency pro-tocol} will be linked with another node from {good bus performance}.

single nodes in our "phrase" graph can be entire noun phrases.


One common issue with a pipeline model is that error may propagate through different stages and accumulate.

the overall structure of the model can be redesigned such that the graphs are already time-aware during ranking.


We can see that each relation has an equivalent set-theoretic definition, which dates back to Montague Semantics (Janssen, 2011).

each language unit x 1 or x 2 is modeled as a set, and is supposedly a subset of the universe D. their relations can be determined by the relation between the two sets.


For example, 36.53% entities in DBpedia do not have type information.

kBs are aways in need of updating and completing.


If different types are treated equally, the structural information between types will be lost.

we use type order to model different relatedness between an entity and its related types, which reflects the structural information of type hierarchy to a certain degree.


The length of a sentence influences the number of concepts in the sentence.

we suggest only measures that are not influenced by the number of concepts in the sentence.